To understand gene function, it is helpful to know when and where it is expressed, and under what circumstances the expression level is affected. Beyond questions of individual gene function are also questions concerning functional pathways and how cellular components work together to regulate and carry out cellular processes. Addressing these questions requires the quantitative monitoring of the expression levels of very large number of genes repeatedly, routinely and reproducibly, while starting with a reasonable number of cells from a variety of sources and under the influences of genetic, biochemical and chemical perturbations.
In order to maximize confidence in gene fragment estimates using oligonucleotide microarrays such as the Affymetrix GeneChip® microarrays, it is necessary to identify arrays that are contaminated with artifacts not representative of expression levels of the fragments of interest. Obtaining reliable estimates of gene expression from raw measurements on microarrays presents several problems due to background contributions, non-specific probe response, possible variation in probe sensitivities and possible non-linear responses of the probes to transcript concentration. While it is recognized that quality control measures should be implemented in generating gene expression data, existing quality control techniques employ limited functionality. These processes lack effective centralized applications to flexibly display search results, process large amounts of data, illuminate the differences between data sources, and automatically identify and address problems.
In many prior art techniques, quality control (QC) has been based upon visual evaluations by a live inspector. A book of standard defective images is assembled and used for comparison for the image under inspection. Basically, the inspector would look for probe level deviations from the expected behavior, then total the number of potentially defective probes across the entire chip to determine whether to pass or fail that chip. Such manual inspection procedures raise a number of problems including, but not limited to: 1) the large number of operator hours are required; 2) the nature of the inspection makes it highly subjective; 3) there can be a continuum between gross artifacts and no artifacts which can affect an operator's decision to flag an array; and 4) certain artifacts such as grid misalignment are difficult to detect visually.
One of the early approaches for instrument-based detection of these defects involved the use of thresholds for brightness and dimness, which was one of the simpler tests. However, some of the images can be very uneven in the background and non-uniform such that the overall signal intensity alone may not be a good test. As a result, other comparisons have been utilized, including evaluation of lines, ratios and profiles.
One of the more critical metrics in assessing a genome chip is the overall chip brightness involving an estimate of the background noise on the chip. The overall chip brightness provides a basis for an automatic pass or fail.
A widely used quality metric for gene expression data involves the use of mismatch (MM) control probe pairs that are identical to their perfect match (PM) partners except for a single base difference in a central position. The MM probe pairs act as specificity controls that allow the direct subtraction of both background and cross-hybridization signals, and allow discrimination between “real” signals and those resulting from non-specific or semi-specific hybridization. (Hybridization of the intended RNA molecules should produce a larger signal for the PM probes than for the MM probes, resulting in patterns that are highly unlikely to occur by chance. The pattern recognition rules are codified in analysis software.) In the presence of even low concentrations of RNA, hybridization of the PM/MM pairs produces recognizable and quantitative fluorescent patterns. The strength of these patterns directly relates to the concentration of the RNA molecules in the complex sample. Thus, PM/MM probe sets should permit the determination of whether a signal is generated by hybridization of the intended RNA molecule. However, some research has shown that a certain percentage of the MM probes are consistently brighter than their corresponding PM probes, and that there is often intensity variation between adjacent MM probes, suggesting that the response of the MM probes may be too transcript-specific to accurately measure background.
Using the PM/MM probe sets, a method has been described in which the expression levels of gene fragments may be modeled on an Affymetrix® GeneChip® microarray according to the following formula:yij=PMij−MMij=θiφj+εij,  (1)where i is the index of the array, j is the index of the probe pair for the fragment under consideration, yij denotes the probe-pair difference, PM is the signal intensity, or value, of the PM probe and MM is the signal intensity, or value, of the MM probe. θi is the model-based expression index (MBEI) of the fragment in array i and φj is the derivative of the response of the jth probe for the fragment with respect to the MBEI. φj is also referred to as the probe sensitivity index (“PSI”) of probe j. εij is the error term. Outliers identified according to this model are sometimes referred to as “Li-Wong outliers”. (See Li, C. and Wong, W. H., “Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection”, PNAS 98(1):31-36, 2001, which is incorporated herein by reference in its entirety.)
In view of the aforementioned problems with the MM probes, a different model for estimating gene expression levels using only PM probes was proposed by Li and Wong (“Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application”, Genome Biology 2(8): research 0032.1-0032.11, 2001, which is incorporated herein by reference in its entirety.) That model isPMij=νj+θiφ′j,  (2)where νj is the baseline response of probe pair j to non-specific hybridization, θi is the MBEI of the fragment in array i, and φj′ is the sensitivity of the PM probe or probe pair j. The parameter estimates are obtained by iteratively fitting θi and νj, φj′, while treating the other set as known. This model does not take into account the background structure which may vary independently of individual probes. Such background variation may be the result of defects such as haze and localized artifacts. As a result, both Li-Wong models can be somewhat limited in their reliability and accuracy.
The above-described metrics are not merely used for chip quality control (QC), but may also be used for process validation and checking scanners, among other tests. If a process change does not affect the metrics, it is likely to not affect the quality. If it does affect the metrics, then there may be a corresponding impact on the quality of the expression data.
Accordingly, the need exists for an improved method and system to reliably determine the quality of gene expression data obtained using microarrays and to exclude data that is unreliable, whether the poor quality results from defects on the microarrays themselves or from instrument-based errors. The present invention is directed to such a system and method.